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PUSHGATEWAY
Push-based metric collection for ephemeral and batch workloads
Apache-2.0
ABOUT
Ephemeral workloads — model training jobs, batch inference pipelines, CI/CD builds, and serverless functions — run for seconds to minutes and finish before Prometheus's pull-based scrape cycle captures their metrics. This leaves operators blind to the performance characteristics of batch operations. Pushgateway solves this by acting as a metrics acceptor that batch processes can push metrics to, which Prometheus then scrapes at its regular interval, ensuring no batch job's metrics are lost.
INTEGRATION GUIDE
1. Capture model training metrics (loss, accuracy, learning rate) from ephemeral batch training runs on spot instances
2. Collect batch inference latency and throughput from short-lived Spark or Ray jobs that complete before the Prometheus scrape window
3. Monitor CI/CD pipeline stages in ML deployment workflows (artifact validation, image build, integration test duration)
4. Track data quality metrics pushed by one-shot ETL jobs that process streaming data into training datasets
5. Gather GPU utilization and cost metrics from ephemeral preemptible instances that run model evaluation tasks
TAGS
prometheusmetricsbatch-jobsci-cdephemeralobservabilityml-monitoring